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Jun 3, 2020 at 10:54 comment added stoic-santiago @nbro One of the quizzes in here claim my question's opening statement. coursera.org/learn/getting-started-with-tensor-flow2 Also, it'd be great if you could go through that paper when you have the time and write an answer based upon it! :)
Jun 2, 2020 at 15:41 history edited nbro CC BY-SA 4.0
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Jun 2, 2020 at 15:41 comment added nbro Anyway, can you please provide the link to the exact Coursera's lecture that claims that? Also, if you want, later, I could read that paper and try to provide an answer based on it. And don't forget to tag me with @nbro next time, so that I see the comment ;)
Jun 2, 2020 at 15:38 comment added nbro @cogito_ai I wouldn't say that SGD can't really escape local minima, but that it can get stuck in other (better?) minima. It's not like simulated annealing, where sometimes you perform a random action to escape local minima. However, it's true that there's some form of stochasticity in SGD that probably helps to explore more the search space. And that's what people probably mean by that sentence. See also ai.stackexchange.com/a/20380/2444.
Jun 2, 2020 at 11:08 comment added stoic-santiago The statement above is from a MOOC on Coursera, while I found more details here - proceedings.mlr.press/v80/kleinberg18a/kleinberg18a.pdf
Jun 2, 2020 at 10:30 comment added nbro Where did you read that SGD is able to escape local minima?
May 31, 2020 at 10:18 comment added Kaivalya Swami This is a popular question. You can find a good explanation in the answer here.
May 31, 2020 at 9:56 history asked stoic-santiago CC BY-SA 4.0